{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 对连续型特征，可以用哪个函数可视化其分布？（给出你最常用的一个即可），并根据代码运行结果给出示例。\n",
    "对于连续性特征，通常用seaborn的distplot函数可视化它的分布。运行代码如下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f980295ef90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "#导入数据集\n",
    "df = pd.read_csv(\"boston_housing.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM  ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18   2.31     0  0.538  6.575  65.2  4.0900    1  296       15   \n",
       "1  0.02731   0   7.07     0  0.469  6.421  78.9  4.9671    2  242       17   \n",
       "2  0.02729   0   7.07     0  0.469  7.185  61.1  4.9671    2  242       17   \n",
       "3  0.03237   0   2.18     0  0.458  6.998  45.8  6.0622    3  222       18   \n",
       "4  0.06905   0   2.18     0  0.458  7.147  54.2  6.0622    3  222       18   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
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       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613524</td>\n",
       "      <td>11.347826</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.083004</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601545</td>\n",
       "      <td>23.310593</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.280574</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.006320</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677082</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.347826   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.310593    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677082   12.000000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.083004  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.280574   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.000000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.000000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.000000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.000000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    int64  \n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    int64  \n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    int64  \n",
      " 9   TAX      506 non-null    int64  \n",
      " 10  PTRATIO  506 non-null    int64  \n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(9), int64(5)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f98061948d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df['MEDV'], bins=30, kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 对两个连续型特征，可以用哪个函数得到这两个特征之间的相关性？根据代码运行结果，给出示例。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2b2a6cf10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "figure = plt.figure(figsize=(10,9))\n",
    "data_corr = df.corr()\n",
    "sns.heatmap(data_corr, annot = True)\n",
    "#用热力图表示两个特征间的相关性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. 如果发现特征之间有较强的相关性，在选择线性回归模型时应该采取什么措施。\n",
    "当发现特征之间有较强的相关性，则可以考虑带正则项的回归模型，去除特征间的相关性。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. 当采用带正则的模型以及采用随机梯度下降优化算法时，需要对输入（连续型）特征进行去量纲预处理。课程代码给出了用标准化（StandardScaler）的结果，请改成最小最大缩放（MinMaxScaler）去量纲 ，并重新训练最小二乘线性回归、岭回归、和Lasso模型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'log_y' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-ffc1b1324d54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mss_X\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mss_y\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mlog_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmms_log_y\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_y\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'log_y' is not defined"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "df = pd.read_csv(\"boston_housing.csv\")\n",
    "df.head()\n",
    "\n",
    "# 数值特征MinMaxScaler处理\n",
    "X = df.drop([\"MEDV\"], axis=1)\n",
    "y = df['MEDV']\n",
    "feat_names = X.columns\n",
    "\n",
    "ss_X = MinMaxScaler()\n",
    "ss_y = MinMaxScaler()\n",
    "mms_log_y = MinMaxScaler()\n",
    "\n",
    "mms_log_y = MinMaxScaler()\n",
    "#对训练数据，先调用fit方法训练模型，得到模型参数；\n",
    "X = ss_X.fit_transform(X)\n",
    "y = ss_y.fit_transform(y.values.reshape(-1,1))\n",
    "log_y = mms_log_y.fit_transform(log_y.values.reshape(-1, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'log_MEDV'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'log_MEDV'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-8916d7dc168d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;31m# 数据准备\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'MEDV'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mlog_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'log_MEDV'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"MEDV\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"log_MEDV\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2798\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2799\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'log_MEDV'"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#评价回归预测模型的性能\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "#画图\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#图形出现在Notebook里而不是新窗口\n",
    "%matplotlib inline\n",
    "\n",
    "df = pd.read_csv(\"boston_housing.csv\")\n",
    "df.head()\n",
    "\n",
    "# 数据准备\n",
    "y = df['MEDV'] \n",
    "log_y = df['log_MEDV']\n",
    "X = df.drop([\"MEDV\", \"log_MEDV\"], axis=1)\n",
    "\n",
    "#特征名称，用于后续显示权重系数对应的特征\n",
    "feat_names = X.columns\n",
    "\n",
    "#将数据分割训练数据与测试数据\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#随机采样20%的数据构建测试样本，其余作为训练样本\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=33, test_size=0.2)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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